Smoking and fracture risk in men: a meta-analysis of cohort studies, using both frequentist and Bayesian approaches

Past studies indicate that men are more likely to smoke and be at higher risk of smoking-related conditions than women. Our research aimed, through meta-analysis, to assess the association between smoking and fracture risk in men. The following databases were searched, including MEDLINE, EMBASE, Scopus, PsycINFO, ISI Web of Science, Google Scholar, WorldCat, and Open Grey, for identifying related studies. A random-effects model was used to pool the confounder-adjusted relative risk (R.R.). Frequentist and Bayesian hierarchical random-effects models were used for the analysis. The heterogeneity and publication bias were evaluated in this study. Twenty-seven studies met the inclusion criteria. Overall, smoking is associated with a significantly increased risk of fracture in both the frequentist approach (R.R., 1.37; 95% confidence interval: 1.22, 1.53) and the Bayesian approach (R.R., 1.36; 95% credible interval: 1.22, 1.54). Significant heterogeneity was observed in the meta-analysis (Higgin's I2 = 83%) and Cochran's Q statistic (p < 0.01). A significant association was also observed in multiple pre-specified sensitivity and subgroup analyses. Similar results were observed in the group containing a large sample size (≥ 10,000 participants), and the group has a small sample size (< 10,000 participants); the pooled R.R was 1.23 (95% confidence interval, 1.07–1.41) and 1.56 (95% confidence interval, 1.37–1.78), respectively. With the Bayesian method, the effect size was 1.23 (95% credible interval, 1.05, 1.45) for the large sample size group and 1.57 (95% credible interval, 1.35, 1.82) for the small sample size group. Smoking is associated with a significant increase in fracture risk for men. Thus, smoking cessation would also greatly reduce fracture risk in all smokers, particularly in men.


Methods
This meta-analysis was conducted in accordance with the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines 21 , with reference to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 22 . The study objectives, primary outcomes, literature search strategy, inclusion and exclusion criteria, study selection methods, data extraction, and data synthesis were all defined in advance in the metaanalysis research protocol (in Supplementary). We also pre-specified the sensitivity and subgroup analyses that we planned to conduct this meta-analysis in the protocol.
Data sources and searches. Two investigators (Y.X. and Y.B.) conducted a comprehensive literature search. Electronic searches were conducted on MEDLINE, using the following terms: men, fractures, osteoporosis, smoking, cigarette, and tobacco, with no restrictions on language, year of publication, or publication status. Using the same strategy, we also conducted literature searches of EMBASE, PsycINFO, SCOPUS, and ISI Web of Science. The above search terms were adapted for other database searches, according to the syntax of each specific database. The last literature search was conducted on April 28, 2021. We also searched Google Scholar, WorldCat Dissertations, and Open Grey. Experienced librarians were consulted to ensure the comprehensiveness of the literature search. Two investigators (M.W. and Y.B.) independently examined reference lists from the original studies and related meta-analyses and reviews. Study selection. The following criteria were used to screen relevant references: (1) prospective or retrospective cohort studies designs; (2) reported smoking status (never, ever, or current smoker); (3) had risk estimates for any fracture or provided sufficient information to estimate fracture risk; and (4) reported results for men. At the initial selection stage, the two investigators independently screened each article's title and abstract retrieved from the electronic search. Only those citations that both reviewers deemed irrelevant were excluded. References with a disagreement between the two reviewers were included for a further full review. In the second phase of the study selection, each reference's full content obtained during the screening stage was reviewed and assessed by the investigators independently. For duplicate publications from the same study cohort, we included in our meta-analysis the study with the largest sample size or effect size adjusted for the largest number of confounders. Disagreements or uncertainties were discussed and resolved through adjudication from a third investigator (Y.X.) when needed. We only included studies that reported relative risk (R.R.) or hazard ratio (H.R.) of fracture associated with smoking or those with the necessary data for calculating R.R. in the current meta-analysis. The agreement between investigators was evaluated using the κ statistic, a robust statistic for inter-rater reliability testing. Study appraisal. The methodologic quality of each included study was scored independently by the two researchers (M.W. and Y.B.), using the Newcastle-Ottawa Scale 23 . No major disagreements or discrepancies arose between the two investigators; minor differences were resolved by rechecking the original reports and by discussion. As recommended by the MOOSE study group 21 , the quality scores were not used as weights in the meta-analysis. However, quality scores were used in the subgroup analysis (score > 7 versus ≤ 7). Data abstraction. The two reviewers (M.W. and Y.B.) performed data extraction independently. Before the study, a standard data abstraction form was developed. The following information was recorded: titles, authors, types of publication (journal article, abstract, or unpublished data), characteristics of study (year of publication, country of origin, inclusion and exclusion criteria, number of participants, number of cases, and duration of follow-up), characteristics of participants (age and race, if applicable), assessment of exposure (smoking), method of ascertainment of outcomes, outcomes (fractures, along with the corresponding regions), and risk estimates (adjusted R.R. and H.R., corresponding 95% confidence intervals, adjustment of confounders, and stratification abstraction). When multiple estimates were presented in the original studies, the estimates with most confounders were adjusted, and the estimate of current smokers was chosen for overall pooled analysis when applicable. Corresponding estimates from the subgroup analyses in the original studies were abstracted when appropriate. One study 24 did not report adequate data to compute the effect size. We attempted to contact the corresponding author for additional information but were unsuccessful. www.nature.com/scientificreports/ Statistical analysis. The summary measures used in this meta-analysis were confounder-adjusted R.R. or H.R. for fractures. For studies that reported the estimates by subgroups only, the overall effect size was estimated by a meta-analysis of the reported subgroup's estimates. Before we pooled the data, R.R. or H.R. was transformed into their natural logarithms in order to stabilize the variance and normalize the distribution. We derived H.R. or R.R. natural logarithm variance from the corresponding 95% CIs provided in the original reports. Both frequentist and Bayesian hierarchical random-effects models were utilized for the synthesis analysis. In the frequentist meta-analysis, the DerSimonian-Laird method 25 was used to calculate the pooled R.R. and variance.
In the Bayesian meta-analysis, Gaussian distribution with an unknown effect size (θ i ) and known within-study variance δ 2 i was assumed for each log R.R. (denoted as φ i ). The set of θ i across the original studies was also assumed to follow a Gaussian distribution, with an unknown mean (μ) and across-study variance (τ 2 ), where μ was the estimate of the overall log R.R. and τ 2 was a measure of the between-study variation. The prior distribution of τ 2 was assumed to follow an improper uniform distribution, and the prior distribution for τ 2 was assumed to be non-informative. The probabilities that current smoking use increases fracture risk by more than 0%, 10%, or 20% were estimated and reported. Heterogeneity was assessed with Cochran's Q statistic and Higgins's index 26,27 . Univariate and multiple meta-regression analysis was performed to explore heterogeneity. Baujat plot was used to identify studies that had high heterogeneity 28 , and the effect size was estimated after removing outlying/influential studies.
Several pre-specified sensitivity analyses were conducted to assess the robustness of our estimates. The effects of current smoking on fracture risk were calculated with different inclusion criteria, including reporting R.R./ H.R., using medical records/hospital dataset, using hip fracture as the outcome, using clinical vertebral fracture as the outcome, and studies focusing on people older than 60 years old. Subgroup meta-analysis stratified by characteristics identified study location, length of follow-up, sample size, year of publication, and quality score. We also conducted a cumulative meta-analysis by performing sequential random-effects pooling, beginning with the earliest qualified report. Each subsequent meta-analysis summarized all eligible reports from the preceding years. To demonstrate the effect of adding reports on the pooled effect size, we presented results chronologically in a forest plot.
A funnel plot created by plotting R.R.s against their standard errors was utilized to examine the potential for publication bias. We also used the Egger test to examine the significance of publication bias. Furthermore, the trim-and-fill method was employed to estimate and adjust unpublished studies' potential effects on the estimated effect size. We used R statistical software (Version 4.0, Core Team, Vienna, Austria) for the data analysis. A p-value of 0.05 or less was considered statistically significant. Fig. 1. After removing duplicate references from different databases, we found a total of 6945 potential references. After investigators Y.B. and M.W. screened titles and abstracts of all these references, 58 full-text research articles were retrieved and assessed for eligibility. The agreement between the two investigators was modest at this initial screening stage (κ = 0.75). After reviewing all full-text articles, twenty-eight studies with fracture data met the inclusion criteria. However, two study reports from the same study team used the same data source but focused on different outcomes 29,30 . Therefore, we combined the two studies as one, and twenty-seven studies were included in the current meta-analysis. Three original studies 31-33 by Drs. Nguyen et al. 31 , Felsenberg et al. 33 , and De Laet et al. 32 , included in a meta-analysis conducted by Dr. Kanis et al. 34 , also met our inclusion criteria. However, the three studies were updated by Drs. Nguyen et al. 35 , Roy et al. 36 , and van der Klift et al. 37 with larger study samples, respectively. Thus we included the three corresponding updated studies [35][36][37] in the meta-analysis. The agreement between the two investigators was good at this second stage (κ = 0.83). All included studies were published in English.

Subgroup analysis.
The fracture risk was slightly higher among studies conducted in North/South America (R.R., 1.54; 95% CI, 1.29-1.84) than studies completed in Europe and other regions. We also found studies with follow-ups of more than five years had a lower R.  Table 1, all of which were quite similar to the results from the frequentist, which indicated that heterogeneity remained high in most subgroup analyses. In the univariate meta-regression, the results of R 2 indicate that two variables, location and reported R.R./H.R., could explain the, 31.34% and 58.61% of the heterogeneity, respectively. There was no multicollinearity between the two mentioned variables, and the heterogeneity in this study was further assessed with multiple meta-regression. After adjusting the two variables and their interaction, the significant heterogeneity among included studies was still observed (I 2 , 58.7%; p-value = 0.0006). The Baujat plot was then used to detect outlier/influential studies, and two 16,41 were identified ( Supplementary Fig. 1). After excluding the two studies, the pooled effect size for the remaining 25 studies was 1.33 (95%CI, 1.24-1.42) in the frequentist approach, and the I 2 was 23.4%, while the p-value for the heterogeneity test was 0.14 ( Supplementary  Fig. 2). www.nature.com/scientificreports/  www.nature.com/scientificreports/  www.nature.com/scientificreports/ Publication bias. Publication bias was examined by plotting the log R.R.s between smokers and non-users against their standard errors for each study (Fig. 4). Visual inspection of the funnel plot indicated that publication bias might be present. The Egger test (p = 0.0024) also indicated significant publication bias in our current meta-analysis. Hence, we employed the trim-and-fill correction to adjust for the publication bias. However, the overall effect size remained significant after the correction (R.R., 1.20; 95% CI, 1.06-1.35).

Discussion
This meta-analysis summarizes 27 cohort studies to assess the association between smoking and fracture risk among men. Both the frequentist method (R.R., 1.37; 95% CI, 1.22, 1.53) and the Bayesian method (R.R., 1.36; 95% CrI, 1.22, 1.54) showed a significant association between smoking and increased fracture risk. The association between smoking and fracture risk was consistent in all sensitivity analyses with different inclusion criteria, various subgroup analyses, and analysis after excluding two outlier/influential studies, which suggests consistency and robustness of findings in this meta-analysis. Furthermore, although the cumulative meta-analysis showed that the pooled estimate fluctuated during 1993-2009, the study also showed a consistent and significant association between smoking and an increased risk of fractures since 2010. This finding suggests that the addition of future studies would have a limited impact on the overall estimate. Additionally, the 95% CIs were increasingly narrower when studies were organized chronologically, which further demonstrates the robustness of our results. Our meta-analysis results are consistent with the previous meta-analysis conducted by Dr. Wu et al., that smoking increases the risk of hip fracture in men 13 . However, the previous meta-analysis was published five years ago and thus was unable to integrate findings from recently published, more extensive studies [14][15][16][17][18][19][54][55][56][57] . Moreover, the previous meta-analysis only assessed smoking and hip fracture association. Therefore, the association between smoking and other, more specific fracture outcomes remains unknown. However, we updated the meta-analysis by including the most recent qualified study reports in the present study. We also quantified the association between smoking and overall fracture, along with vertebral fractures. In addition, we included two crucial updated study reports in the present meta-analysis, both with large sample sizes. Dr. Forsen and colleagues published an eligible study in 1994 58 , which was included in the previous meta-analysis. The same research group published another updated report with the same data in 1998; the corresponding updates were included in the present meta-analysis. We also replaced the original study by Paganini-Hill et al. 59 in the previous meta-analysis, with an updated report by White et al. 43 , in the present meta-analysis. Although both study reports used the same data source, the newer version was more comprehensive. It assessed the association between smoking and hip, wrist, and spine fracture, while the older one only focused on hip fracture, so the current study included the updated findings. Compared to the previous meta-analysis, we employed both the frequentist and Bayesian approaches to evaluate the association between smoking and fracture risk. The results from the two methods were consistent in our study. In addition, the Bayesian meta-analysis provided the probabilities that smoking increases fracture risk by 10% and 20%; such results help male smokers to recognize that smoking is linked to elevated fracture risk. Our findings are also consistent with a prior meta-analysis by Dr. Kanis and his colleagues 34 that current male smokers had a significantly higher fracture risk than non-current smokers. Compared to Dr. Kanis and colleagues' meta-analysis, our meta-analysis not only included all additional recent eligible studies but also replaced three original studies by Drs. Nguyen et al. 31 , Felsenberg et al. 33 , and De Laet et al. 32 in Dr. Kanis's meta-analysis with the corresponding updated studies by Drs. Nguyen et al. 35 , Roy et al. 36 , and van der Klift et al. 37 , respectively. The three updated studies had a larger sample size, which might contribute to a more www.nature.com/scientificreports/ precise estimate because the variance and standard error decrease as the sample size increaseds 60 . Thus, our meta-analysis is likely to yield a more accurate estimate of the effect size. The underlying mechanism of how smoking influences fracture risk is not fully understood. One potential reason could be the decreased bone mineral density (BMD) caused by smoking 61 . Low BMD is the primary cause of osteoporotic fracture risk and is a measure widely used in clinical practice to identify patients at an increased risk of fracture 56 . The biological plausibility of BMD loss due to smoking can be linked to the effects of nicotine and cadmium in cigarette smoke on bone cells 61 . In addition, smoking is associated with decreased vitamin D levels. People with low vitamin D are more likely to have low BMD and are at a higher risk of suffering a fracture 62 . On the other hand, smoking is also associated with reducing calcium absorption 54 , also leading to increased fracture risk. Another potential reason is that smoking has been considered a risk factor for injury 57 , which is linked to fractures. A study in elderly persons found a 28% increase in smokers' accidental injury over non-smokers, and smoking and nicotine are inhibitory factors in wound and fracture healing 55 . Smoking also interferes with tissue repair processes, leaving tissue more susceptible to injury and fracture 55 .
Our study has several limitations. First, two studies with self-reported data and one without specification about the outcome measures were included. The data from the three studies might be less reliable compared to other data derived directly from medical records. After removing the three studies, the effect size of smoking on fracture risk decreased slightly. Second, due to the different questionnaire designs from the included studies, we could not examine the dose-response relationship between smoking and the risk of fractures. Third, publication bias is suspected in the current meta-analysis, as indicated by the funnel plot and Egger test. However, the pooled estimate remained significant after we adjusted for publication bias by using the trim-and-fill method. Finally, the adjustment for confounders in all the included articles varies, which may exaggerate or underestimate the findings. Nevertheless, this limitation unlikely altered our meta-analyses conclusion; the consistent findings from sensitivity and subgroup analyses suggested that our current study findings are reliable and robust.

Conclusion
In summary, our comprehensive meta-analysis found a significant association between smoking and increased risk of fractures. Our findings were consistent in both frequentist and Bayesian approaches, as well as all subgroup analyses, sensitivity analysis, and the analysis with publication bias correction. More importantly, our results have crucial implications in public health, with the most apparent being that quitting smoking can reduce an individual's risk of bone fracture, both now and later in life.